Housekeeping Briefing note to band council back today with feedback

Briefing note to band council – back today with feedback Housekeeping

MIDTERM – FEB 17!

•All readings* covered, with emphasis on portions I presented in class

•Format: short & medium answers, a long answer

Peterman 2004 ICES Journal of Marine Science, 61: 1331-1343

This is process and observation error, which combine to ``Mortality Limit Uncertainty`` in today`s grizzly bear example

Uncertainty in Management

1. Process Error: natural variation

2. Observation (measurement) Error:

Most error refers to uncertainty, not mistakes

Important implications because the consequences of ignoring uncertainty can be great

3. Model Selection Error:

use of “more wrong” model

Why Grizzly Bears?

• Species with vulnerable life history

• High Conservation Value

• Most human-caused mortality is trophy hunting via

special permits (so, “manageable” by Ministry of Environment)

• Contentious debate, especially in coastal BC, and without sufficient facts

Confronting error and uncertainty in wildlife management: grizzly bear mortality in British Columbia, Canada

•

Much unknown/uncertain about populations

Our objectives

• Examined 2 subsets of uncertainty

– Implementation error

– Process & Observation error – “mortality limit uncertainty”

• First, distinguish between mortality limits and mortality targets

Limit vs. target

• Mortality Limit:

– from biology of species

– has Process Error about it; nature is variable

– our estimate adds Observation error; hard to measure basic population parameters well

– exceeding it should be avoided if you do not want population declines

• Mortality Target: -

The two are often used interchangeably in wildlife management, but are fundamentally different

Target setting by Ministry….though they are really estimated limits (they did not differentiate)

Total mortality target/limit = (Population Estimate × Annual Allowable Mortality)

Targets set for each Grizzly Bear Population Unit (GBPU) for each allocation period

• 2001-2004 • 2004-2006 • 2007-2011

− Estimated Unreported Mort.

Our “audit” (to assess implementation error)

•

Difference between allowable (target/limit) vs.

reported (actual) human-caused mortality

Combined (female + total)

overmortality events

across about 50 GBPUs, 2001-2011

2001-2003 2004-2006 2007-2011

• Actual mortality follows a distribution around the target (we now know about implementation error in action)

Target

Simulations: Confronting Uncertainty

•

Part 1. How does process/observation

uncertainty in population parameters propagate

to uncertainty in mortality limits?

Total mortality target/limit = (Population Estimate × Annual Allowable Mortality) − Estimated Unreported Mort.

Varied Parameter 1: Population Estimates

Ministry’s own estimates have changed -89% to +130% in less than 10 years

We varied population estimates by up to ± 40% in both

directions from values the Ministry uses

Varied Parameter 2: Annual Allowable Mortality (i.e.,

what proportion can we kill to ensure the population will not decline?)

Maximum:

•

5.7% (Bunnel & Tait)

•

2-3% (Sidorowicz & Gilbert 1981)

•

4.9% (McLouglin 2003) (0% in “low quality”

habitat)

•

7.8% (McCullough 1981 and 1986)

Ministry currently uses a max is 6%; we varied from of 4% to 8%

Varied Parameter 3: Unreported Mortality

•

Could be as high as 25% of human-caused

mortality (Peek

et al.

1987)

•

In Alaska, only half of grizzly hunting was

reported (Miller 1990)

•

26% of kills from McLellan et al. 1999

meta-analysis of BC populations

•

We

varied

unreported mortality by

up to ±

50% in both directions

from values Ministry

uses

Simulations (to confront Process and Observation Error..i.e., uncertainty)

•

Simulate limits by

randomly varying

each

parameters

within ranges of uncertainty

:

– Population Estimate: ± 40% around what Min uses – AAM: ± 2% around what Min uses

– Unreported Mortality: ± 50% around what Min uses

Repeat 1000 times to create distribution of LIMITS for each GBPU (i.e. run those equations 1000 times (ie, ‘runs’) & get 1000 different answers for the limit)

Total mortality target/limit = (Population Estimate × Annual Allowable Mortality) − Estimated Unreported Mort.

Distribution of simulated LIMITS from 1000 runs Previous Limit (Target) used by Ministry Distribution of actual limit Limit (# of bears) % oc curr en ce in simula ti on runs

Part 2. Simulations to confront Implementation Error

•

Implementation error similarly follows a

distribution

•

Constructed from historic relationship

between allowable and reported mortality

Randomly sample these values 1000 times to create distribution of TARGETS for each GBPU

Target

Distributions of actual mortality based on TARGETS from 1000 runs Distribution of actual mortality around target (implementation error) % oc curr en ce in simula ti on runs Target (#s of bears)

“Probabilistic” framework for setting NEW, SAFER targets that incorporate all sources of estimated error and uncertainty

• Part 3 is overlaying Parts 1 & 2 together

• Incorporates both implementation error (around targets) and

Probabilistic framework for setting

targets

•

For a given %

probability of

overmortality (x),

set target such

that

x % of limit

distribution

overlaps with

target

distribution

•

Can shift target

based on

acceptable risk

5% probability of overmortality

10% probability of overmortality

10%

We can move the new ‘target range’ to find a level of harvest ‘we’ like;

New Target Previous Limit (Target) used by Ministry Distribution of actual limit Target/Limit (#s of bears) Distribution of actual mortality around target (implementation error) Distribution of actual mortality around target (implementation error) % oc curr en ce in simula ti on runs

5% probability of overmortality

Difference in target required to bring the probability of overmortality (i.e., target > limit) to below 5%

Distribution of actual mortality around target (implementation error) % oc curr en ce in simula ti on runs Target/Limit (#s of bears)

Using this new approach - How would changes to hunting affect the probability of overmortlity (under 5%)?

-reducing hunting by half would have reduced overmortality events by 86%

-completely eliminating

hunting would have reduced overmortality by 96%

Confronting uncertainty is both necessary and….

useful in that it derived a better scientific way to set “mortality targets” Yay, science…we now know how many bears “should” be killed

for trophy!

Resource management, however, is also about incorporating values

Bear hunting ban declared by 10 B.C. First Nations

But provincial government says only it has the authority to issue such a ban The Canadian Press Sep 13, 2012 6:46 AM PT